Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images

Computer vision may aid in melanoma detection. We sought to compare melanoma diagnostic accuracy of computer algorithms to dermatologists using dermoscopic images. We conducted a cross-sectional study using 100 randomly selected dermoscopic images (50 melanomas, 44 nevi, and 6 lentigines) from an in...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Journal of the American Academy of Dermatology Jg. 78; H. 2; S. 270 - 277.e1
Hauptverfasser:	Marchetti, Michael A., Codella, Noel C.F., Dusza, Stephen W., Gutman, David A., Helba, Brian, Kalloo, Aadi, Mishra, Nabin, Carrera, Cristina, Celebi, M. Emre, DeFazio, Jennifer L., Jaimes, Natalia, Marghoob, Ashfaq A., Quigley, Elizabeth, Scope, Alon, Yélamos, Oriol, Halpern, Allan C.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Inc 01.02.2018
Schlagworte:	Algorithms computer algorithm computer vision Congresses as Topic Cross-Sectional Studies dermatologist Dermatologists Dermoscopy Diagnosis, Computer-Assisted Humans International Skin Imaging Collaboration International Symposium on Biomedical Imaging Lentigo - diagnostic imaging Machine Learning melanoma Melanoma - diagnosis Melanoma - pathology Nevus - diagnostic imaging reader study ROC Curve skin cancer Skin Neoplasms - diagnostic imaging Skin Neoplasms - pathology International Symposium on Biomedical Imaging skin cancer reader study dermatologist ISIC ROC melanoma SVM machine learning ISBI computer vision International Skin Imaging Collaboration computer algorithm
ISSN:	0190-9622, 1097-6787, 1097-6787
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!